Method for creating a data input file for increasing the efficiency of the aviation environmental design tool (AEDT)

ABSTRACT

A method of increasing the efficiency of the Aviation Environmental Design Tool (AEDT) by using a computer algorithm to generate an input file with far fewer flight tracks than would normally be required to obtain the same AEDT results using the same data pool.

FIELD

This disclosure relates to the field of noise pollution analysis andremediation. More particularly, this disclosure relates to determiningnoise contours caused by aircraft around airports.

BACKGROUND

Airports and Air Navigation Service Providers (“ANSPs”, such as the USFederal Aviation Administration (FAA)) have a regular need to calculatethe noise “contours” around airports. These contours define the specificnoise impact around an airport, neighborhood by neighborhood, street bystreet. The annual average decibel level of impact at a given locationis a critical piece of information needed by airports and ANSPs becausegovernment programs are in place to pay for noise mitigation measuresdepending on the decibel levels from aircraft noise pollution in a givenlocation. Large financial decisions are made based upon correctcalculation of this information.

The FAA has created a software program called the Aviation EnvironmentalDesign Tool (AEDT) to help calculate these noise contours based on aninput of “flight track” data, which is information about what types ofaircraft have departed and arrived at an airport and the precise pathand altitudes of each track. More specifically, flight track data isinformation about a flight (such as the aircraft/engine type, whether itwas a departure or an arrival, what runway it used) and then each of thepoints of that departure or arrival (where each point contains fourcomponents: time/latitude/longitude/altitude).

Typically, airports and ANSPs are required to generate an annual contourincluding noise contour data which is often represented as a series ofpolygons surrounding an airport indicating the average noise exposurelevel across the year for areas surrounding the airports. Differentfeatures and complexities exist in tailoring these contours, includingthe decibel ranges (e.g., 65 db, 70 db, 75 db), the type of decibellevels used (e.g., “A-weighted” decibels to approximate how the humanear reacts to noise), and adjustment for evening and night time hournoise as those have greater impact on residents. AEDT is not only usedto generate the contours of noise based on existing operations at anairport, but also projected future contours based on predicted airtraffic plans, or ad-hoc contours based on proposed ideas such as addinga runway or changing where aircraft will fly near an airport.

Consultants use AEDT to generate contours for airports and ANSPs, butAEDT is cumbersome and considerably slow to use. AEDT requires specialexpertise plus patience in waiting for the software to run. Nonetheless,AEDT is the de facto standard for accurate noise contour results. AEDTruns slower the more “flight tracks” are fed into it. For example, if anairport as 200,000 flight operations a year, and a user wishes togenerate a contour representative of the use at that airport, the userwould feed 200,000 flight tracks into the AEDT software and wait manyhours or even days for the software to run and generate contours.

What is needed, therefore, is a method to reduce the wait time whenusing AEDT while at the same time, ensuring that the resulting noisecontours are accurate.

SUMMARY

The above and other needs are met by a method to minimize the number offlight tracks that are input to AEDT with the AEDT result still beingsubstantially the same as if all flight tracks were input to AEDT. Forthe example described above, the method would select a fraction (e.g.,10%) of the 200,000 flight tracks occurring during a year but theselection is made to ensure that the AEDT output is substantially thesame as if all 200,000 flight tracks were input to AEDT. The methodreduces data not merely via random sample, but by several key processesthat allow the method to generate a sample that accurately representsthe data. This is based on knowledge of AEDT built into the method thatwill ultimately provide a random sample that AEDT will considerequivalent to the full data set.

The method described herein includes the ability for a user to specifymany parameters to assist in creating the most accurate reduced dataset. This flexibility is necessary to account for differences atdifferent airports, and differences in the types of aircraft thatoperate at each airport (and what times of day they fly). Times of dayare important because the results that come out of AEDT are sensitive toaircraft flying at different times of day. Optimization combined withthe ability to automate intelligent customization could save untoldamounts of time and money.

In a preferred embodiment, a method of generating an input file in acomputable readable format for use with the Aviation EnvironmentalDesign Tool (AEDT) to increase the efficiency of the AEDT includes thesteps of: (a) running a software program on a computer wherein thesoftware program is stored on a computer readable medium incommunication with the computer and wherein the software programincludes an algorithm for performing specific steps; (b) inputting userinput data to the computer using a graphical user interface incommunication with the computer, wherein the user input data comprisesinformation in the form of values related to specific variablesassociated with flights at a specific airport; (c) determining aplurality of unique factors using the computer based on the user inputdata, wherein each unique factor comprises a unique combination ofvariables from the user input data; (d) accessing a database incommunication with the computer wherein the database includes flighttrack data stored thereon, such flight track data including data relatedto flight tracks of aircraft arriving and departing at a specificairport for a specific period of time; (e) querying the database usingthe computer to determine how many aircraft arrivals or departures meeteach unique factor combination of variables for each unique factor; (f)running the algorithm to perform the steps of: (1) calculating an exactnumber of flight tracks to query the database for each unique factor;and (2) determining weight values to assign to the flight tracks foreach unique factor; (g) querying a random sample of flight tracks in thedatabase for each unique factor based on the calculated exact number offlight tracks and the determined weight values; and (h) generating aninput file comprising all flight track data associated with the randomsample of flight tracks queried by the computer.

The method preferably further includes an initial step of collectingflight track data and storing such flight track data in a database incommunication with the computer.

The step of inputting user input data may further include inputting userinput data in the form of weight values for specific variables whereinmore weight is given to queried data that meets specific criteria.

The step of accessing the database may further include detecting andadjusting anomalies in the flight track data.

The method may further include the step of determining an ideal numberof flight tracks for selection based on the user input data including avariable of average daily flight operations multiplied by a variable ofnumber of days for such flight operations.

The method may further include the steps of: querying the database todetermine the total number of aircraft departures and arrivals byaircraft type for a specified time period; and creating a departureweight value based on the total number of aircraft departures andarrivals equaling total number of arrivals divided by the total numberof departures during the specific time period; creating an arrivalweight value based on the total number of aircraft departures andarrivals equaling total number of departures divided by the total numberof arrivals during the specific time period; and adjusting a percentageof aircraft arrivals versus departures using the algorithm by factoringin the departure weight and the arrival weight.

In another aspect, embodiments of the disclosure provide a method ofgenerating an input file in a computable readable format for use withthe Aviation Environmental Design Tool (AEDT) to increase the efficiencyof the AEDT, the method comprising the steps of: (a) receiving userinput data on a computer, the user input data including values relatedto specific variables associated with flights at a specific airport; (b)determining a plurality of unique factors using the computer based onthe user input data, wherein each unique factor comprises a uniquecombination of variables from the user input data; (c) accessing adatabase in communication with the computer wherein the databaseincludes flight track data stored thereon, such flight track dataincluding data related to flight tracks of aircraft arriving anddeparting at a specific airport for a specific period of time; (d)querying the database using the computer to determine how many aircraftarrivals or departures meet each unique factor combination of variablesfor each unique factor; (e) calculating an exact number of flight tracksto query the database for each unique factor; (f) determining weightvalues to assign to the flight tracks for each unique factor; and (g)generating a file comprising all flight track data associated with therandom sample of flight tracks queried by the computer.

In another aspect, embodiments of the disclosure provide a method ofprocessing an input file in a computable readable format for use withthe Aviation Environmental Design Tool (AEDT), the method comprising thesteps of: (a) running AEDT software on a first computer; and (b)processing an input file using the first computer running the AEDTsoftware wherein the input file is in a computable readable format whichis compatible with the AEDT software and wherein the input file wascreated using the steps of: (1) determining a plurality of uniquefactors using a second computer based on user input data, wherein eachunique factor comprises a unique combination of variables from the userinput data; (2) accessing a database in communication with the secondcomputer wherein the database includes flight track data stored thereon,such flight track data including data related to flight tracks ofaircraft arriving and departing at a specific airport for a specificperiod of time; (3) querying the database using the second computer todetermine how many aircraft arrivals or departures meet each uniquefactor combination of variables for each unique factor; (4) calculatingan exact number of flight tracks to query the database for each uniquefactor; and (5) determining weight values to assign to the flight tracksfor each unique factor, wherein the input file comprises all flighttrack data associated with the random sample of flight tracks queried bythe second computer.

In some embodiments, the first computer comprises the second computer.

The method preferably further includes a step of producing a noisecontour file based on the processed input file wherein the noise contourfile includes noise contour data that is substantially the same as noisecontour data that would have been produced using the AEDT software ifall flight tracks from the database had been included in the input file.

The summary provided herein is intended to provide examples ofparticular disclosed embodiments and is not intended to cover allpotential embodiments or combinations of embodiments. Therefore, thissummary is not intended to limit the scope of the invention disclosurein any way, a function which is reserved for the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Further features, aspects, and advantages of the present disclosure willbecome better understood by reference to the following detaileddescription, appended claims, and accompanying figures, wherein elementsare not to scale so as to more clearly show the details, wherein likereference numbers indicate like elements throughout the several views,and wherein:

FIG. 1 shows a schematic of a system for accomplishing embodiments ofthe methods described herein;

FIG. 2 shows a schematic of a computer system on which the presentinvention may be implemented;

FIG. 3 shows a flow chart including method steps used for accomplishingembodiments of the method described herein; and

FIG. 4 shows a flow chart of an algorithm used in the method stepslisted in FIG. 3.

The figures are provided to illustrate concepts of the inventiondisclosure and are not intended to embody all potential embodiments ofthe invention. Therefore, the figures are not intended to limit thescope of the invention disclosure in any way, a function which isreserved for the appended claims.

DETAILED DESCRIPTION

Various terms used herein are intended to have particular meanings. Someof these terms are defined below for the purpose of clarity. Thedefinitions given below are meant to cover all forms of the words beingdefined (e.g., singular, plural, present tense, past tense). If thedefinition of any term below diverges from the commonly understoodand/or dictionary definition of such term, the definitions belowcontrol.

FIG. 1 shows a basic embodiment of a system 100 for creating a datainput file for increasing the efficiency of the Aviation EnvironmentalDesign Tool (AEDT). The system 100 includes a computer 200 which isshown in more detail in FIG. 2. The system 100 including the associatedcomputer 200 follow method steps described herein and shown in FIG. 3and FIG. 4 to prepare an input data file that can be fed to the AEDT toincrease the efficiency of the AEDT by reducing the amount of data fedto a computer running AEDT software. However, the method is accomplishedin such a way that the resultant reduced data set causes the AEDT tooutput substantially the same output data as if a full (non-reduced)data set had been input to the computer running the AEDT software.Because less data is input to the AEDT, it runs faster and, therefore,more efficiently. (When referring to “the AEDT”, it should be understoodthat AEDT is a program stored on a computer readable medium and run on acomputer.)

In order to obtain a reduced dataset that will cause the AEDT to outputaccurate results, a software program 102 stored on a computer readablemedium in communication with or otherwise stored on the computer 200 isprovided. In order to run the software program 102, flight track datamust be gathered from one or more sources. Such data is then saved toone or more databases 104A on a local network in communication with thecomputer 200, one or more databases 104B on the cloud or generally onthe Internet in communication with the computer 200, one or moredatabases 104C on the computer 200 itself, or a mobile data storagedevice 106 (e.g., a USB stick) (hereinafter, collectively, “the database104”). Data can also be entered through a graphical user interface (GUI)108 in communication with the computer 200. The gathered flight trackdata is usually in the form of radar data feeds that provide, for eachflight track, the latitude, longitude, and altitude of every point inthe flight track along with the exact time of that point. The gathereddata also preferably contains information about the aircraft type thatwas flown for each track, whether the flight was an arrival ordeparture, and the origin/destination airport. After further methodsteps, a resultant reduced data set is created which can be stored onthe computer 200 or sent to be stored on the cloud 110, a mobile storagedevice 112 (e.g., a USB stick), or another computer or database 114.

FIG. 2 shows an example of the computer 200 that can be used toaccomplish certain steps of various embodiments of the method describedherein. The computer includes a central processing unit (“CPU”) 202 andmemory 204 in the form of computer-readable memory, such as randomaccess memory (“RAM”) and read-only memory (“ROM”). Program informationor data is stored on the memory 204 and may include operating systemcode for the computer 200 and application code for applications operableon the computer 200. The computer 200 may further include primary andsecondary storage 206, such as optical disk storage and/or magnetic diskstorage. Program information and other data may also be stored on thesecondary storage 206.

The computer 200 includes a network connection means 208 forcommunicating with a network, such as a local area network (“LAN”) orthe Internet. The computer 200 further preferably includes one or moreinput devices 210, such as a keyboard, mouse, scanner, touchscreen,voice input, and other various devices for receiving input on thecomputer 200. Input is received on the computer 200 through the one ormore input devices 210, such as text, images, graphics, and othervarious inputs. The computer 200 further includes one or more outputdevices such as a display 212 for receiving output from a video adapter214 of the computer 200. Other various output devices may include, forexample, a printer, sound output, video output, and other variousoutputs.

With reference to FIG. 3, a first step 300 of a preferred embodiment ofthe method includes gathering flight track data. The flight track datais saved to the database 104. An optional second step 302 includesdetecting and adjusting anomalies in the flight track data. This step302 can be thought of as a step to “clean up” the flight track data bydetecting clearly incorrect data points and smoothing out the data usinga data smoothing algorithm such as, for example, a Smoothing Spline orLocal Polynomial Regression. In addition, and most importantly for AEDT,the flight track data is sometimes incomplete in that the tracks do notfully reach a runway. In these cases, an additional track segment ispreferably created to attach the particular flight to a runway. This canbe accomplished manually through the graphical user interface 108 orautomatically by the software program 102. In difficult cases (e.g., thetrack data is missing too many points and is too far from an airport),the particular flight track data featuring the anomaly is preferablyignored by the software program 102. For example, the program wouldconsider a minimum distance from the airport for a track to be valid,and if it is the software would simply add a synthetic track point onthe runway connecting it to the first point, with a time valuecalculated based on the computed speed of the flight. Each track sent toAEDT in the resultant reduced data set is “weighted”. For example, if aparticular flight track has a weight of “2”, that means the resultantreduced data set sent to AEDT causes AEDT to pretend there are twotracks in the input that are identical to this single flight track. AEDTcan process a single flight with a weight of “2” faster than it canprocess two identical tracks each with a weight of “1”. However, in bothcases, the resulting output data from AEDT will be the same. The goal isto reduce a number of flight tracks, setting weight values accordingly.

Another step 304 of a preferred embodiment of the method includescollecting user input data. This is accomplished using the graphicaluser interface 108. Table 1 provided below includes a first column ofvariables discussed in more detail herein, a second column of examplevalues, and a third column providing some description of the particularvariable.

TABLE 1 Example Values Description Required Variables Name JFK01Arbitrary name for this study Database JFKAirport Database name,typically the airport StartDate Jan. 1, 2017 First Date of data EndDateDec. 31, 2017 Last Date of data (typically a year after Start)AvgDailyOps 300 How may flight operations (arrivals or departures) thisairport typically has in a day NumOfDays 10 Number of days to reducedata to. E.g., 36.5 days means approx 1/10th the data queried than afull 365 days MinTracksPerFactor 5 Minimum number of tracks to query foreach unique combination of factors, to make sure we have an accuraterepresentation MaxTracksPerFactor 300 Maximum number of tracks to queryfor each unique combination of factors, so we don't waste time queryingmore than we need DepArrEqual Yes Force departure and arrival counts tobe equal for each unique aircraft type Optional Variables FactorArrDepYes Factor based on Arrival/Departure FactorAirport No Factor based onAirport FactorRunway Yes Factor based on Runway name FactorDEN YesFactor based on DEN (Day/Evening/Night) FactorAirlineClass No Factorbased on Airline Class FactorAircraftType No Factor based on AircraftType FactorAircraftClass No Factor based on Aircraft ClassFactorAircraftClass2 No Factor based on Aircraft Class2 FactorProcedureNo Factor based on Procedure FactorINMTrack No Factor based on INM TrackFactorINMType Yes Factor based on INM Type (Exact model of aircraft)ExtraWeight1 DEN, N , 2 Given additional weight to flights that matchthis field and value. ExtraWeight2 AircraftClass2, J, 2 Given additionalweight to flights that match this field and value . . . ExtraWeightNGiven additional weight to flights that match this field and valueFilterArrDep Restrict to just data to just Arrivals or DeparturesFilterAirport JFK Restrict to just this airport FilterRunway 4L, 4R,13L, 13R Restrict to one or more runways FilterDEN Restrict to Day,Evening or Night FilterAirlineClass Restrict to a particular AirlineClass FilterAircraftType Restrict to a particular Aircraft TypeFilterAircraftClass Restrict to a particular “Aircraft Class”FilterAircraftClass2 Restrict to a particular “Aircraft Class2”FilterProcedure Restrict to a particular flight procedure FilterINMTrackRestrict to a particular INM Track association FilterINMType Restrict toa particular INM Type (Exact model of aircraft)

A user manually inputs the user input data to the computer 200. Ofspecial note is the ExtraWeight1, ExtraWeight2, . . . ExtraWeightNvalues (there can be as many ExtraWeight values as the user wishes tospecify). This feature is important because it allows a user to placemore emphasis or “weight” on a particular variable. For example, theExtraWeight1 variable in Table 1 is set so that the subcategory of “N”(Night) from the category of “DEN” (Day-Evening-Night) is given aweighted value of “2” instead of 1. As such, more emphasis will beplaced on flights that fit this category (i.e., flights that arrive ordepart during the “N” (Night) period). Note that this added emphasisdoes not mean that the AEDT results will be skewed via consideration ofmore “N” (Night) flights than is proportionally represented in theflight track data, but instead it is used to ensure that enough of astatistical sample is gathered for this category so that there is asufficient representational set of track points for this category.

Step 306 includes balancing the number of arrivals and departures peraircraft type. The variable “INM Type” represents the unique aircrafttype and model. In theory, each INM Type should have the same number ofarrivals and departures for the selected timeframe (e.g., a year) at agiven airport. But the flight track data may be missing information, andsuch information needs to be balanced out if information is missing.This is accomplished by selecting (e.g., via SQL query to the database104) the count of unique “Departure or Arrival” and INM Typecombinations over a selected period of time (e.g., a year). The resultsof the search will provide two new variables per INM Type including“depWeight” and “arrWeight”. “depWeight” is the departure weight and isset to “1” unless there are less departures than arrivals for this INMType, in which case it is set to #arrivals/#departures for this INMType. “arrWeight” is the arrival weight and is set to “1” unless thereare less arrivals than departures for this INM Type, in which case it isset to #departures/#arrivals for this INM Type. For example, if for agiven INM Type, there are 10 departures and 7 arrivals, then depWeightwill be “1” and arrival weight will be 10/7=1.42857. The depWeight andarrWeight values are used later in further calculations.

Step 308 includes determining unique factors and row counts per uniquefactor. A key to reducing the amount of data sent to the AEDT is toquery “counts” of information across the entire selected time period(e.g., a year) for “unique factors” of information, the results of whichare used to generate more specific queries returning a reduced amount ofdata.

A “unique factor” is a set of values for a specific combination ofvariables specified in the user input (Step 304). For example, if a userspecified three variables including “ArrDep” (Arrival, Departure),“Runway”, and “DEN” (aka Day, Evening or Night), there would necessarilybe a minimum number of unique factors. Every flight must be an arrivalor departure (2 values), must use a runway, and must depart or arrive inthe day, evening or night (3 values). If there are two runways (2values) at the specified airport (01 and 19), in this scenario, therewould be 12 unique factors. All 12 combinations are shown in Table 2below.

TABLE 2 Arrival, Runway 01, Day Arrival, Runway 01, Evening Arrival,Runway 01, Night Arrival, Runway 19, Day Arrival, Runway 19, EveningArrival, Runway 19, Night Departure, Runway 01, Day Departure, Runway01, Evening Departure, Runway 01, Night Departure, Runway 19, DayDeparture, Runway 19, Evening Departure, Runway 19, Night

There may be far more than 12 unique factors, and a number of uniquefactors may depend on how many variables are chosen by the user in theuser interface. In fact, INM Type (or INM_TYPE) must be added as afactor so that all the different aircraft types passing through anairport can be considered. When INM_TYPE is added to the above example,then the number of unique factors would be equal to 12 times the numberof unique aircraft models that flew at this airport during the specifiedtime period.

All the unique factors and the row counts (i.e., how many flightoperations there were in the time period) for that unique factor set canbe determined by performing an SQL Query on the database 104. A samplequery might look like the following, for the above example factors+INMType for the year 2017:

-   SELECT RUNWAY, DEN, INMTYPE_ID, COUNT(*) as CNT FROM FLIGHT_DATA

WHERE datetime_da BETWEEN ‘2017-01-01 00:00:00’ AND ‘2017-12-3123:59:59’

AND RUNWAY IN (‘01, '19’)

GROUP BY RUNWAY,DEN,INMTYPE_ID

ORDER BY RUNWAY,DEN,INMTYPE_ID

The result of this query will provide a count of flight operationsthroughout the year for each unique set of factors that have beenselected. This will serve as the basis for how to query a much smalleramount of data but keep similar proportions to what occurred throughoutthe year. Note that in this query the value of two new variables thatwill be used below in further steps can be determined. Those newvariables include the following:

InmTypeTotDep=Total # of departures for this INM Type

InmTypeTotArr=Total # of arrivals for this INM Type

TotalNumTracks=total number of flight tracks for the year for thisairport

“FLIGHT_DATA” is broadly defined as a table of all flight tracks storedon the database 104, such data having different columns for differentcharacteristics of the data (e.g., aircraft type, arrival/departure,Day-Evening-Night (DEN)).

A next step 310 includes determining the ideal number of flight tracks(“IdealNumTracks”) to use with the AEDT In other words, this step is toidentify how many tracks are to be queried under ideal circumstances toarrive at substantially the same output using the AEDT as if all flighttracks had been put into the AEDT This is accomplished based on inputgiven in Step 304, namely “AvgDailyOps”, i.e., the number of operations(departures+arrivals) the particular airport has in a typical day, and“NumOfDays”, i.e., the ideal number of days for the calculation to beminimized to. This will provide an ideal number of flight tracks to becreated. This calculation is made using Equation 1 below:IdealNumTracks=AvgDailyOps*NumOfDays  Equation 1

For example, if it is known that an airport has 2,000 operations perday, and a goal is to use 5 days' worth of data, then about 10,000flight tracks should be sent to the AEDT If the specified airport has100,000 operations per year, then about 10% of the flight tracks fromthe database 104 need to be queried. Note that in this example the useof “approximately” 10,000 tracks—it can be plus or minus some flighttracks to account for certain factors and make sure the data isrepresentational of all 100,000 flight tracks.

Step 312 includes determining the number of flight tracks to query andthe “weight” to be allocated to each track belonging to each particularunique factor. Step 312 takes all the information obtained or calculatedin prior steps to determine two variables per unique factor as follows:

-   ExactNumTracks: the number of flight tracks to query for this unique    factor-   WeightFinal: the weight value to be sent to AEDT for each track    belonging to this unique factor

For example, a unique factor might be as follows: Runway 01, Evening,Arrival, INM Type 32

In this example, through the course of the year, there were 500 flightsmatching that unique factor. Based on previous calculations, the goal isto query a 10% random sample of the data, so ExactNumTracks might be 50,and the WeightFinal would be 0.1, meaning these 50 tracks represents 0.1times the 500 tracks available for the year for this unique factor. Inreality, however the process is not so simple because of the complexnature of getting the most exact representation and weighting, based notonly on the data but on a user's desire to give extra weight to certainvariables. This complexity is represented in the steps shown in FIG. 4which are discussed in more detail below regarding Step 312.

Step 314 includes querying a random sample of data on the database 104for each unique factor. For each unique factor, calculations have beenmade to determine the ExactNumTracks to query and the WeightFinal valueto use for the AEDT At this point, a random sample of data must bequeried that meets each unique factor criteria, such that the randomsample returns ExactNumTracks rows. To do this most efficiently, thefirst query includes the following:

-   SELECT OPER_ID from FLIGHT_DATA where . . . (data meets a specific    unique factor and date range)

OPER_ID is a unique number for every flight track in a data table offlight track data generated by the query listed above. This list ofOPER_IDs is stored in memory 204 in the code's variables and then, usingthe software program 102 in conjunction with the computer 200, OPER_IDsare randomly selected from the list until the computer 200 hasExactNumTracks of the OPER_IDs stored in memory 204 in the code'svariables. For example, if the particular unique factor being queriedhas 500 tracks during the applicable year, the SELECT statement abovewill return 500 OPER_ID values. But if ExactNumTracks is 50, then thesoftware program 102 on the computer 200 randomly selects 50 of thoseOPER_IDs. Once the set of 50 random OPER_IDs are stored, a query isconducted on the database 104 for ALL columns in the FLIGHT_DATA tablefor just those OPER_IDs, thus providing all the flight track datarequired for that specific unique factor. Each row of data returned isassigned a “weight”, found in a column called WEIGHT which is theWeightFinal value for this unique factor. The selected flight track dataand WeightFinal values are stored in memory 204 in the code's variables.Step 314 is preferably repeated for every unique factor.

A final step 316 involves creating an AEDT input file—the file that issubstantially smaller than the file of all flight tracks that wouldotherwise have been used but for the software program 102. At thispoint, there are rows of flight track data and associated WEIGHT valuesthat have been stored. The flight track data is translated into theformat required by AEDT which is generally an XML file format withspecific tags, such format known to persons having ordinary skill in theart. The formatted file includes the random sample of flight track data,with each flight track containing a unique WEIGHT value to represent howmany times each particular track should be counted by the AEDT. WEIGHTvalues need not be whole numbers. The system 100 can calculate a WEIGHTvalue of, for example, 5.573, meaning that that particular flight trackshould be considered to have occurred 5.573 times during the selectedtime period (typically a year).

With reference back to Step 312 and FIG. 4, a first sub step 400 of step312 is calculating an initial value for percentage of tracks to query(“PercInitial”) defined below in Equation 2. This is done by dividingthe ideal number of tracks (“IdealNumTracks”) calculated in step 310using Equation 1.PercInitial=IdealNumTracks/TotalNumTracks  Equation 2

Next, substep 402 includes calculating the total number of tracks ofarrivals and departures (“TotalTracksAD”) defined below in Equation 3.TotalTracksAD=TotalNumTracks/2  Equation 3

Substep 404 includes calculating an adjusted value for the percentage ofarrivals and departures (“PercAdjDepArr”) calculated using Equation 4below. “depWeight” and “arrWeight” were given in Step 306.PercAdjDepArr=PercInitial*depWeight*arrWeight  Equation 4

Sub step 406 includes creating UserWeights for each ExtraWeight inputvalue and assigning values to the UserWeights. The ExtraWeight input wascreated/input in Step 304. The value of each UserWeight should be equalto “1” if there is no match with that variable or the given weight valueif there is a match. For example, if a user added an ExtraWeight of “2”when DEN=“E”, then if the applicable unique factor has a DEN value of“E” the UserWeight would be set to “2”. If not, the UserWeight would beset to “1”. There should be one UserWeight created for each ExtraWeightthat the user entered in Step 304. For example, if there is aExtraWeight1 and an ExtraWeight2, there should also be a UserWeight1 andUserWeight2.

Substep 408 includes calculating the Total User Weight(“TotalUserWeight”) for the unique factor wherein Total User Weightequals all of the UserWeight values multiplied together. In an examplein which there is a UserWeight1 of 1.3 and a UserWeight2 of 2, the TotalUser Weight would be 2.6.

Substep 410 includes calculating the percentage of adjusted weights(“PercAdjWeights”) based on the adjusted percentage of arrivals anddepartures and the Total User Weight using Equation 5 below.PercAdjWeights=PercAdjDepArr*(1/TotalUserWeight)  Equation 5

Substep 412 includes calculating an adjusted number of tracks(“NumTracksAdj”) which equals the total number of tracks(“TotalNumTracks”) multiplied by percentage of adjusted weights(“PercAdjWeights”).

At this point, in substep 414, the adjusted minimum number of tracks(“NumTracksAdjMin”) is set equal to the adjusted number of tracks(“NumTracksAdj”) calculated in substep 412. At substep 416, adetermination is made as to whether the adjusted minimum number oftracks (“NumTracksAdjMin”) is less than the minimum number of tracks perfactor (“MinTracksPerFactor”). If so, in substep 418, the adjustedminimum number of tracks (“NumTracksAdjMin”) is set equal to the minimumnumber of tracks per factor (“MinTracksPerFactor”).

Next, at substep 420, the adjusted percentage of minimum tracks(“PercAdjMin”) is calculated according to Equation 6. The purpose ofthis equation is to take the current “percentage of tracks to use” valuefor this unique factor and adjust it based on a possible change innumber of tracks due to not reaching a minimum. PercAdjMin is fed intothe calculation for PercAdjFinal in a later substep described belowshown in Equation 7.PercAdjMin=PercAdjWeights*(NumTracksAdj/NumTracksAdjMin)  Equation 6

At substep 422 the adjusted maximum number of tracks (“NumTracksAdjMax”)is set equal to the adjusted minimum number of tracks(“NumTracksAdjMin”). In substep 424, a determination is made as towhether the adjusted maximum number of tracks (“NumTracksAdjMax”) isgreater than maximum number of tracks per factor. If so, in substep 426,the adjusted maximum number of tracks (“NumTracksAdjMax”) is set equalto the maximum number of tracks per factor (“MaxTracksPerFactor”).

At this point, in substep 428, a final adjusted percentage of tracks(“PercAdjFinal”) is calculated using Equation 7 below. The purpose ofthis equation is to take the current “percentage of tracks to use” valuefor this unique factor and adjust it based on a possible change in thenumber of tracks due to having too many tracks (i.e., greater than themaximum per factor). PercAdjFinal is later fed into the calculation atthe next step for FloatNumTracks.PercAdjFinal=PercAdjMin*(NumTracksAdjMin/NumTracksAdjMax)  Equation 7

In substep 430, “FloatNumTracks” is calculated by multiplying the totaltracks of arrivals and departures (“TotalTracksAD”) by the adjustedfinal percentage of tracks (“PercAdjFinal”). FloatNumTracks is thenumber of tracks to be queried for this unique factor, taking intoaccount all the prior reductions and minimum/maximum restrictions. Forexample, there may be 500 tracks for this unique factor (e.g., for thisrunway/INM_Type/DEN combination), but FloatNumTracks might beapproximately 1/10th of that, or, for example, 50.6, after allcalculations are completed.

In substep 432, the exact number of tracks (“ExactNumTracks”) iscalculated by rounding up the FloatNumTracks to the next whole number.This is done because FloatNumTracks may not be a whole number, based onall the calculations. But there are always a whole number of tracks inthe database. So, for example, if FloatNumTracks is 50.6, ExactNumTrackswould be 51.

Next, in substep 434, weight value for the tracks (“WeightForTracks”) iscalculated according to Equation 8 below. Since the exact number oftracks is a rounded value of the calculated ideal number of tracks, wehave to calculate what weighting adjustment is necessary to account forthe percentage difference between the two.WeightForTracks=FloatNumTracks/ExactNumTracks  Equation 7

Finally, in substep 436, the final weight value for the tracks(“WeightFinal”) is calculated as the total user weight(“TotalUserWeight”) from substep 408 multiplied by the weight value forthe tracks (“WeightForTracks”). Thus, at this point, there is an exactnumber of tracks (“ExactNumTracks”) that has been calculated as well asa final weight value (“WeightFinal”) to be attributed to the tracks.These two values are then used in subsequent step 314 as discussedabove.

The previously described embodiments of the present disclosure have manyadvantages, including substantially reducing the amount of time it takesthe AEDT to calculate noise contours for an airport using flight trackdata from that airport, such flight track data taken over an extendedperiod of time (most often, a year). The method generates an AEDT inputfile with significantly less flight tracks, but some or all of thoseflight tracks are weighted to varying degrees to ensure that, when usingthe generated input file, output from the AEDT would be substantiallythe same as if all flight tracks had been input to the AEDT. The systemand method described herein also provides a way for a user to add extraemphases to one or more variables of flight track data in the generatedinput file for specific flight tracks, thus tailoring the generatedinput data to variables that are the most important to the particularuser.

The foregoing description of preferred embodiments of the presentdisclosure has been presented for purposes of illustration anddescription. The described preferred embodiments are not intended to beexhaustive or to limit the scope of the disclosure to the preciseform(s) disclosed. Obvious modifications or variations are possible inlight of the above teachings. The embodiments are chosen and describedin an effort to provide the best illustrations of the principles of thedisclosure and its practical application, and to thereby enable one ofordinary skill in the art to utilize the concepts revealed in thedisclosure in various embodiments and with various modifications as aresuited to the particular use contemplated. All such modifications andvariations are within the scope of the disclosure as determined by theappended claims when interpreted in accordance with the breadth to whichthey are fairly, legally, and equitably entitled.

What is claimed is:
 1. A method of generating an input file in acomputable readable format for use with the Aviation EnvironmentalDesign Tool (AEDT) to increase the efficiency of the AEDT, the methodcomprising the steps of: a. running a software program on a computerwherein the software program is stored on a computer readable medium incommunication with the computer and wherein the software programincludes an algorithm for performing specific steps; b. inputting userinput data to the computer using a graphical user interface incommunication with the computer, wherein the user input data comprisesinformation in the form of values related to specific variablesassociated with flights at a specific airport; c. determining aplurality of unique factors using the computer based on the user inputdata, wherein each unique factor comprises a unique combination ofvariables from the user input data; d. accessing a database incommunication with the computer wherein the database includes flighttrack data stored thereon, such flight track data including data relatedto flight tracks of aircraft arriving and departing at a specificairport for a specific period of time; e. querying the database usingthe computer to determine how many aircraft arrivals or departures meeteach unique factor combination of variables for each unique factor; f.running the algorithm to perform the steps of: i. calculating an exactnumber of flight tracks to query the database for each unique factor;and ii. determining weight values to assign to the flight tracks foreach unique factor; g. querying a random sample of flight tracks in thedatabase for each unique factor based on the calculated exact number offlight tracks and the determined weight values; and h. generating aninput file comprising all flight track data associated with the randomsample of flight tracks queried by the computer.
 2. The method of claim1 further comprising an initial step of collecting flight track data andstoring such flight track data in a database in communication with thecomputer.
 3. The method of claim 1 wherein the step of inputting userinput data further comprises inputting user input data in the form ofweight values for specific variables wherein more weight is given toqueried data that meets specific criteria.
 4. The method of claim 1wherein the step of accessing the database further comprises the step ofdetecting and adjusting anomalies in the flight track data.
 5. Themethod of claim 1 further comprising the step of determining an idealnumber of flight tracks based on the user input data including avariable of average daily flight operations multiplied by a variable ofnumber of days for such flight operations.
 6. The method of claim 1further comprising the steps of: a. querying the database to determinethe total number of aircraft departures and arrivals by aircraft typefor a specified time period; and b. creating a departure weight valuebased on the total number of aircraft departures and arrivals equalingtotal number of arrivals divided by the total number of departuresduring the specific time period; c. creating an arrival weight valuebased on the total number of aircraft departures and arrivals equalingtotal number of departures divided by the total number of arrivalsduring the specific time period; and d. adjusting a percentage ofaircraft arrivals versus departures using the algorithm by factoring inthe departure weight and the arrival weight.
 7. The method of claim 5further comprising selecting flight tracks based on the user input dataincluding a variable of average daily flight operations multiplied by avariable of number of days for such flight operations.
 8. A method ofgenerating an input file in a computable readable format for use withthe Aviation Environmental Design Tool (AEDT) to increase the efficiencyof the AEDT, the method comprising the steps of: a. receiving user inputdata on a computer, the user input data including values related tospecific variables associated with flights at a specific airport; b.determining a plurality of unique factors using the computer based onthe user input data, wherein each unique factor comprises a uniquecombination of variables from the user input data; c. accessing adatabase in communication with the computer wherein the databaseincludes flight track data stored thereon, such flight track dataincluding data related to flight tracks of aircraft arriving anddeparting at a specific airport for a specific period of time; d.querying the database using the computer to determine how many aircraftarrivals or departures meet each unique factor combination of variablesfor each unique factor; e. calculating an exact number of flight tracksto query the database for each unique factor; f. determining weightvalues to assign to the flight tracks for each unique factor; and g.querying a random sample of flight tracks in the database for eachunique factor based on the calculated exact number of flight tracks andthe determined weight values; h. generating a file comprising all flighttrack data associated with the random sample of flight tracks queried bythe computer.
 9. A method of processing an input file in a computablereadable format for use with the Aviation Environmental Design Tool(AEDT), the method comprising the steps of: a. running AEDT software ona first computer; b. processing an input file using the first computerrunning the AEDT software wherein the input file is in a computablereadable format which is compatible with the AEDT software and whereinthe input file was created using the steps of: i. determining aplurality of unique factors using a second computer based on user inputdata, wherein each unique factor comprises a unique combination ofvariables from the user input data; ii. accessing a database incommunication with the second computer wherein the database includesflight track data stored thereon, such flight track data including datarelated to flight tracks of aircraft arriving and departing at aspecific airport for a specific period of time; iii. querying thedatabase using the second computer to determine how many aircraftarrivals or departures meet each unique factor combination of variablesfor each unique factor; iv. calculating an exact number of flight tracksto query the database for each unique factor; and v. determining weightvalues to assign to the flight tracks for each unique factor.
 10. Themethod of claim 9 wherein the first computer comprises the secondcomputer.
 11. The method of claim 9 further comprising the step ofproducing a noise contour file based on the processed input file whereinthe noise contour file includes noise contour data that is substantiallythe same as noise contour data that would have been produced using theAEDT software if all flight tracks from the database had been includedin the input file.